If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.
If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started!
The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

審閱

DS

I'm very new at this theme, this course has being the perfect beginning. If you don't have a mathematical background and you don't understand when the funny S appear, this is the course for you!

AA

Jul 27, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

A great introduction to understanding research and a great platform to springboard keen clinicians into performing their own research. Will take what I've learnt and apply it to my own research!

從本節課中

Building an intuitive understanding of statistical analysis

There is hardly any healthcare professional who is unfamiliar with the p-value. It is usually understood to have a watershed value of 0.05. If a research question is evaluated through the collection of data points and statistical analysis reveals a value less that 0.05, we accept this a proof that some significant difference was found, at least statistically.In reality things are a bit more complicated than that. The literature is currently full of questions about the ubiquitous p-vale and why it is not the panacea many of us have used it as. During this week you will develop an intuitive understanding of concept of a p-value. From there, I'll move on to the heart of probability theory, the Central Limit Theorem and data distribution.

教學方

Juan H Klopper

腳本

In this section, I want to discuss distributions. Now, data points for a variable, they have populations accompanying certain patterns. And data points from a sample might follow that same pattern. It is going to come in some form. Now, these patterns we call distributions. Now, we're all familiar with some form of distribution. Think about grading on a curve. We might have all been students and at some or other time we were graded on a curve or we grade our own students on a curve. Now, that is usually called the normal curve, what we use at least to grade people on a normal curve. Now, if you think about the hemoglobin levels in the healthy human population, that's also going to form some distribution. That's also going to form on some curve. Some values are going to occur more commonly than others. It is going to follow a certain distribution. That's what we normally see. The normal distribution. Peaks in the middle and peaks out to the sides. This is a frequency distribution. In other words, some values are going to occur more commonly than others. If we grade students and we want to give them a 70% mark, most students will get 70%. Less and less students will get 80% or 60%. Even fewer will get much more than that, 90 on the one side, 50% on the other side. The further away from the mean the values occurred this commonly. Same would go for hemoglobin. Imagine in a healthy population, the normal hemoglobin is about 14, in other words, 14 is gonna occur very commonly and as we move away from 14, those values will become less and less common. Now there is another type of distribution. Remember this was in a sample or in a population there is something else called a sampling distribution. It's also going to follow some pattern. It's going to follow some pattern, but it's something a bit different. The one I'm going to talk about most often using example is a sampling distribution of sampling means. It means we've discussed it before. You remember the central limit theorem. Some values will occur more commonly than others. Some means will occur more commonly than others. If you were to repeatedly take many many samples and take the means, some means will occur more commonly than others. And that will be a sampling distribution. So in this section we'll discuss two forms of distributions, one that is just a normal distribution in a population sample, and the other one is going to be sampling distributions.